Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations230119
Missing cells235352
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.2 MiB
Average record size in memory256.0 B

Variable types

Numeric17
Categorical11
Text4

Alerts

def_penalty is highly overall correlated with def_penalty_yardsHigh correlation
def_penalty_yards is highly overall correlated with def_penaltyHigh correlation
def_qb_hits is highly overall correlated with def_sack_yards and 1 other fieldsHigh correlation
def_sack_yards is highly overall correlated with def_qb_hits and 1 other fieldsHigh correlation
def_sacks is highly overall correlated with def_qb_hits and 1 other fieldsHigh correlation
def_tackles is highly overall correlated with def_tackles_soloHigh correlation
def_tackles_for_loss is highly overall correlated with def_tackles_for_loss_yardsHigh correlation
def_tackles_for_loss_yards is highly overall correlated with def_tackles_for_lossHigh correlation
def_tackles_solo is highly overall correlated with def_tacklesHigh correlation
position is highly overall correlated with position_groupHigh correlation
position_group is highly overall correlated with positionHigh correlation
season_type is highly overall correlated with weekHigh correlation
week is highly overall correlated with season_typeHigh correlation
season_type is highly imbalanced (75.1%)Imbalance
def_fumbles_forced is highly imbalanced (87.8%)Imbalance
def_interceptions is highly imbalanced (86.7%)Imbalance
def_tds is highly imbalanced (96.5%)Imbalance
def_fumbles is highly imbalanced (99.2%)Imbalance
def_fumble_recovery_own is highly imbalanced (98.8%)Imbalance
def_fumble_recovery_opp is highly imbalanced (89.8%)Imbalance
def_safety is highly imbalanced (98.8%)Imbalance
player_name has 124595 (54.1%) missing valuesMissing
headshot_url has 109886 (47.8%) missing valuesMissing
def_fumble_recovery_yards_own is highly skewed (γ1 = 98.87929602)Skewed
def_fumble_recovery_yards_opp is highly skewed (γ1 = 22.15369397)Skewed
def_tackles has 29880 (13.0%) zerosZeros
def_tackles_solo has 39957 (17.4%) zerosZeros
def_tackles_with_assist has 173784 (75.5%) zerosZeros
def_tackle_assists has 112462 (48.9%) zerosZeros
def_tackles_for_loss has 185400 (80.6%) zerosZeros
def_tackles_for_loss_yards has 206176 (89.6%) zerosZeros
def_sacks has 201981 (87.8%) zerosZeros
def_sack_yards has 203330 (88.4%) zerosZeros
def_qb_hits has 188368 (81.9%) zerosZeros
def_interception_yards has 221933 (96.4%) zerosZeros
def_pass_defended has 181209 (78.7%) zerosZeros
def_fumble_recovery_yards_own has 230000 (99.9%) zerosZeros
def_fumble_recovery_yards_opp has 227993 (99.1%) zerosZeros
def_penalty has 197162 (85.7%) zerosZeros
def_penalty_yards has 197476 (85.8%) zerosZeros

Reproduction

Analysis started2024-08-09 02:44:32.779604
Analysis finished2024-08-09 02:45:17.503072
Duration44.72 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

season
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.2405
Minimum1999
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:17.560124image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2000
Q12005
median2011
Q32018
95-th percentile2022
Maximum2023
Range24
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.2240908
Coefficient of variation (CV)0.0035918582
Kurtosis-1.2064194
Mean2011.2405
Median Absolute Deviation (MAD)6
Skewness-0.030384193
Sum4.6282466 × 108
Variance52.187488
MonotonicityIncreasing
2024-08-08T20:45:17.660730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2021 10126
 
4.4%
2023 9992
 
4.3%
2022 9907
 
4.3%
2014 9367
 
4.1%
2017 9353
 
4.1%
2015 9353
 
4.1%
2016 9291
 
4.0%
2018 9278
 
4.0%
2020 9275
 
4.0%
2009 9256
 
4.0%
Other values (15) 134921
58.6%
ValueCountFrequency (%)
1999 8640
3.8%
2000 8587
3.7%
2001 8644
3.8%
2002 9088
3.9%
2003 8923
3.9%
2004 9154
4.0%
2005 9040
3.9%
2006 9073
3.9%
2007 9017
3.9%
2008 9061
3.9%
ValueCountFrequency (%)
2023 9992
4.3%
2022 9907
4.3%
2021 10126
4.4%
2020 9275
4.0%
2019 9228
4.0%
2018 9278
4.0%
2017 9353
4.1%
2016 9291
4.0%
2015 9353
4.1%
2014 9367
4.1%

week
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5756978
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:17.760826image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q314
95-th percentile17
Maximum22
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.312338
Coefficient of variation (CV)0.55477294
Kurtosis-1.1497254
Mean9.5756978
Median Absolute Deviation (MAD)5
Skewness0.018685602
Sum2203550
Variance28.220935
MonotonicityNot monotonic
2024-08-08T20:45:17.860920image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
17 13763
 
6.0%
15 13625
 
5.9%
16 13597
 
5.9%
2 13500
 
5.9%
14 13479
 
5.9%
1 13456
 
5.8%
12 13375
 
5.8%
13 13352
 
5.8%
3 13177
 
5.7%
11 12800
 
5.6%
Other values (12) 95995
41.7%
ValueCountFrequency (%)
1 13456
5.8%
2 13500
5.9%
3 13177
5.7%
4 12581
5.5%
5 12243
5.3%
6 12112
5.3%
7 11904
5.2%
8 11893
5.2%
9 11731
5.1%
10 12292
5.3%
ValueCountFrequency (%)
22 97
 
< 0.1%
21 948
 
0.4%
20 1888
 
0.8%
19 3591
 
1.6%
18 4715
 
2.0%
17 13763
6.0%
16 13597
5.9%
15 13625
5.9%
14 13479
5.9%
13 13352
5.8%

season_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
REG
220589 
POST
 
9530

Length

Max length4
Median length3
Mean length3.0414134
Min length3

Characters and Unicode

Total characters699887
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG 220589
95.9%
POST 9530
 
4.1%

Length

2024-08-08T20:45:17.965019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:18.044090image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
reg 220589
95.9%
post 9530
 
4.1%

Most occurring characters

ValueCountFrequency (%)
R 220589
31.5%
E 220589
31.5%
G 220589
31.5%
P 9530
 
1.4%
O 9530
 
1.4%
S 9530
 
1.4%
T 9530
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 699887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 220589
31.5%
E 220589
31.5%
G 220589
31.5%
P 9530
 
1.4%
O 9530
 
1.4%
S 9530
 
1.4%
T 9530
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 699887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 220589
31.5%
E 220589
31.5%
G 220589
31.5%
P 9530
 
1.4%
O 9530
 
1.4%
S 9530
 
1.4%
T 9530
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 699887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 220589
31.5%
E 220589
31.5%
G 220589
31.5%
P 9530
 
1.4%
O 9530
 
1.4%
S 9530
 
1.4%
T 9530
 
1.4%
Distinct8403
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:18.269796image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length47
Median length10
Mean length9.9942725
Min length1

Characters and Unicode

Total characters2299872
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1319 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
00-0021140 271
 
0.1%
00-0022161 258
 
0.1%
00-0018227 252
 
0.1%
00-0026190 252
 
0.1%
00-0005322 248
 
0.1%
00-0000741 231
 
0.1%
00-0020535 228
 
0.1%
00-0027647 226
 
0.1%
00-0022045 226
 
0.1%
00-0000585 225
 
0.1%
Other values (8393) 227702
98.9%
2024-08-08T20:45:18.609123image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1036360
45.1%
- 229957
 
10.0%
2 205455
 
8.9%
3 161325
 
7.0%
1 128209
 
5.6%
4 99812
 
4.3%
5 93577
 
4.1%
6 91124
 
4.0%
9 89144
 
3.9%
7 88389
 
3.8%
Other values (4) 76520
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2299872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1036360
45.1%
- 229957
 
10.0%
2 205455
 
8.9%
3 161325
 
7.0%
1 128209
 
5.6%
4 99812
 
4.3%
5 93577
 
4.1%
6 91124
 
4.0%
9 89144
 
3.9%
7 88389
 
3.8%
Other values (4) 76520
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2299872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1036360
45.1%
- 229957
 
10.0%
2 205455
 
8.9%
3 161325
 
7.0%
1 128209
 
5.6%
4 99812
 
4.3%
5 93577
 
4.1%
6 91124
 
4.0%
9 89144
 
3.9%
7 88389
 
3.8%
Other values (4) 76520
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2299872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1036360
45.1%
- 229957
 
10.0%
2 205455
 
8.9%
3 161325
 
7.0%
1 128209
 
5.6%
4 99812
 
4.3%
5 93577
 
4.1%
6 91124
 
4.0%
9 89144
 
3.9%
7 88389
 
3.8%
Other values (4) 76520
 
3.3%

player_name
Text

MISSING 

Distinct3120
Distinct (%)3.0%
Missing124595
Missing (%)54.1%
Memory size1.8 MiB
2024-08-08T20:45:18.843359image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length19
Median length17
Mean length8.3778666
Min length5

Characters and Unicode

Total characters884066
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)0.4%

Sample

1st rowA.Vinatieri
2nd rowA.Vinatieri
3rd rowT.Brady
4th rowT.Brady
5th rowT.Brady
ValueCountFrequency (%)
j.jones 483
 
0.5%
c.jones 477
 
0.4%
d.jones 463
 
0.4%
j.jenkins 455
 
0.4%
j.peppers 359
 
0.3%
d.johnson 325
 
0.3%
m.adams 316
 
0.3%
t.johnson 313
 
0.3%
a.jones 303
 
0.3%
j.johnson 299
 
0.3%
Other values (3118) 102298
96.4%
2024-08-08T20:45:19.188224image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 105533
 
11.9%
e 61658
 
7.0%
n 53686
 
6.1%
a 53614
 
6.1%
o 49629
 
5.6%
r 49541
 
5.6%
s 40519
 
4.6%
l 40234
 
4.6%
i 39984
 
4.5%
t 24076
 
2.7%
Other values (46) 365592
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 884066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 105533
 
11.9%
e 61658
 
7.0%
n 53686
 
6.1%
a 53614
 
6.1%
o 49629
 
5.6%
r 49541
 
5.6%
s 40519
 
4.6%
l 40234
 
4.6%
i 39984
 
4.5%
t 24076
 
2.7%
Other values (46) 365592
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 884066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 105533
 
11.9%
e 61658
 
7.0%
n 53686
 
6.1%
a 53614
 
6.1%
o 49629
 
5.6%
r 49541
 
5.6%
s 40519
 
4.6%
l 40234
 
4.6%
i 39984
 
4.5%
t 24076
 
2.7%
Other values (46) 365592
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 884066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 105533
 
11.9%
e 61658
 
7.0%
n 53686
 
6.1%
a 53614
 
6.1%
o 49629
 
5.6%
r 49541
 
5.6%
s 40519
 
4.6%
l 40234
 
4.6%
i 39984
 
4.5%
t 24076
 
2.7%
Other values (46) 365592
41.4%
Distinct8220
Distinct (%)3.6%
Missing227
Missing (%)0.1%
Memory size1.8 MiB
2024-08-08T20:45:19.448477image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length27
Median length24
Mean length12.8884
Min length6

Characters and Unicode

Total characters2962940
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1260 ?
Unique (%)0.5%

Sample

1st rowRahim Abdullah
2nd rowRahim Abdullah
3rd rowRahim Abdullah
4th rowRahim Abdullah
5th rowRahim Abdullah
ValueCountFrequency (%)
williams 5138
 
1.1%
smith 4052
 
0.9%
chris 3997
 
0.9%
johnson 3412
 
0.7%
jones 3385
 
0.7%
mike 3349
 
0.7%
thomas 2709
 
0.6%
brandon 2622
 
0.6%
michael 2551
 
0.6%
brown 2471
 
0.5%
Other values (6064) 427517
92.7%
2024-08-08T20:45:19.809871image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 256673
 
8.7%
a 243619
 
8.2%
231311
 
7.8%
n 228406
 
7.7%
r 213471
 
7.2%
o 186325
 
6.3%
i 172662
 
5.8%
l 146206
 
4.9%
s 127608
 
4.3%
t 95249
 
3.2%
Other values (46) 1061410
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2962940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 256673
 
8.7%
a 243619
 
8.2%
231311
 
7.8%
n 228406
 
7.7%
r 213471
 
7.2%
o 186325
 
6.3%
i 172662
 
5.8%
l 146206
 
4.9%
s 127608
 
4.3%
t 95249
 
3.2%
Other values (46) 1061410
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2962940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 256673
 
8.7%
a 243619
 
8.2%
231311
 
7.8%
n 228406
 
7.7%
r 213471
 
7.2%
o 186325
 
6.3%
i 172662
 
5.8%
l 146206
 
4.9%
s 127608
 
4.3%
t 95249
 
3.2%
Other values (46) 1061410
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2962940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 256673
 
8.7%
a 243619
 
8.2%
231311
 
7.8%
n 228406
 
7.7%
r 213471
 
7.2%
o 186325
 
6.3%
i 172662
 
5.8%
l 146206
 
4.9%
s 127608
 
4.3%
t 95249
 
3.2%
Other values (46) 1061410
35.8%

position
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)< 0.1%
Missing322
Missing (%)0.1%
Memory size1.8 MiB
DE
36743 
CB
30193 
DT
28789 
OLB
25707 
DB
20638 
Other values (24)
87727 

Length

Max length3
Median length2
Mean length2.1796151
Min length1

Characters and Unicode

Total characters500869
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOLB
2nd rowOLB
3rd rowOLB
4th rowOLB
5th rowOLB

Common Values

ValueCountFrequency (%)
DE 36743
16.0%
CB 30193
13.1%
DT 28789
12.5%
OLB 25707
11.2%
DB 20638
9.0%
SS 15388
6.7%
FS 15205
6.6%
ILB 13616
 
5.9%
LB 13348
 
5.8%
NT 7713
 
3.4%
Other values (19) 22457
9.8%

Length

2024-08-08T20:45:19.932508image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 36743
16.0%
cb 30193
13.1%
dt 28789
12.5%
olb 25707
11.2%
db 20638
9.0%
ss 15388
6.7%
fs 15205
6.6%
ilb 13616
 
5.9%
lb 13348
 
5.8%
nt 7713
 
3.4%
Other values (19) 22457
9.8%

Most occurring characters

ValueCountFrequency (%)
B 113756
22.7%
D 86176
17.2%
L 58900
11.8%
S 46084
9.2%
T 40243
 
8.0%
E 38847
 
7.8%
C 31101
 
6.2%
O 25945
 
5.2%
F 15740
 
3.1%
I 13616
 
2.7%
Other values (10) 30461
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 113756
22.7%
D 86176
17.2%
L 58900
11.8%
S 46084
9.2%
T 40243
 
8.0%
E 38847
 
7.8%
C 31101
 
6.2%
O 25945
 
5.2%
F 15740
 
3.1%
I 13616
 
2.7%
Other values (10) 30461
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 113756
22.7%
D 86176
17.2%
L 58900
11.8%
S 46084
9.2%
T 40243
 
8.0%
E 38847
 
7.8%
C 31101
 
6.2%
O 25945
 
5.2%
F 15740
 
3.1%
I 13616
 
2.7%
Other values (10) 30461
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 113756
22.7%
D 86176
17.2%
L 58900
11.8%
S 46084
9.2%
T 40243
 
8.0%
E 38847
 
7.8%
C 31101
 
6.2%
O 25945
 
5.2%
F 15740
 
3.1%
I 13616
 
2.7%
Other values (10) 30461
 
6.1%

position_group
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing322
Missing (%)0.1%
Memory size1.8 MiB
DB
81488 
DL
73124 
LB
58928 
WR
 
5538
OL
 
4338
Other values (4)
 
6381

Length

Max length4
Median length2
Mean length2.001349
Min length2

Characters and Unicode

Total characters459904
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLB
2nd rowLB
3rd rowLB
4th rowLB
5th rowLB

Common Values

ValueCountFrequency (%)
DB 81488
35.4%
DL 73124
31.8%
LB 58928
25.6%
WR 5538
 
2.4%
OL 4338
 
1.9%
RB 3090
 
1.3%
TE 2107
 
0.9%
QB 1029
 
0.4%
SPEC 155
 
0.1%
(Missing) 322
 
0.1%

Length

2024-08-08T20:45:20.057625image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:20.170926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
db 81488
35.5%
dl 73124
31.8%
lb 58928
25.6%
wr 5538
 
2.4%
ol 4338
 
1.9%
rb 3090
 
1.3%
te 2107
 
0.9%
qb 1029
 
0.4%
spec 155
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 154612
33.6%
B 144535
31.4%
L 136390
29.7%
R 8628
 
1.9%
W 5538
 
1.2%
O 4338
 
0.9%
E 2262
 
0.5%
T 2107
 
0.5%
Q 1029
 
0.2%
S 155
 
< 0.1%
Other values (2) 310
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 459904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 154612
33.6%
B 144535
31.4%
L 136390
29.7%
R 8628
 
1.9%
W 5538
 
1.2%
O 4338
 
0.9%
E 2262
 
0.5%
T 2107
 
0.5%
Q 1029
 
0.2%
S 155
 
< 0.1%
Other values (2) 310
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 459904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 154612
33.6%
B 144535
31.4%
L 136390
29.7%
R 8628
 
1.9%
W 5538
 
1.2%
O 4338
 
0.9%
E 2262
 
0.5%
T 2107
 
0.5%
Q 1029
 
0.2%
S 155
 
< 0.1%
Other values (2) 310
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 459904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 154612
33.6%
B 144535
31.4%
L 136390
29.7%
R 8628
 
1.9%
W 5538
 
1.2%
O 4338
 
0.9%
E 2262
 
0.5%
T 2107
 
0.5%
Q 1029
 
0.2%
S 155
 
< 0.1%
Other values (2) 310
 
0.1%

headshot_url
Text

MISSING 

Distinct4007
Distinct (%)3.3%
Missing109886
Missing (%)47.8%
Memory size1.8 MiB
2024-08-08T20:45:20.386121image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length82
Median length82
Mean length81.961375
Min length81

Characters and Unicode

Total characters9854462
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique555 ?
Unique (%)0.5%

Sample

1st rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/nftbfn32n3iukep3biiv
2nd rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/nftbfn32n3iukep3biiv
3rd rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/nftbfn32n3iukep3biiv
4th rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/nftbfn32n3iukep3biiv
5th rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/nftbfn32n3iukep3biiv
ValueCountFrequency (%)
https://static.www.nfl.com/image/private/f_auto,q_auto/league/c7tpbqsqzbg4vw5axw71 271
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/s8bqh2tllr3pyz03fvrx 258
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/wbrve7zrrqiuknohpecm 252
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/gohbupnj3mhletq96zzu 252
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/ykgtrwxckxvxdvadgqjf 226
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/aphgunfzg6ptehfuk8j1 226
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/xirozqwhvfycz5m830yf 221
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/lumuwuwq9kdemsw5uefz 217
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/caovj6onwsoepaen8tjc 216
 
0.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/jbcyxaoosrmj0pn5ynjb 214
 
0.2%
Other values (3997) 117880
98.0%
2024-08-08T20:45:20.685393image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 916177
 
9.3%
/ 841631
 
8.5%
a 801117
 
8.1%
e 553122
 
5.6%
o 447814
 
4.5%
u 442847
 
4.5%
w 442001
 
4.5%
i 433008
 
4.4%
. 360699
 
3.7%
l 325816
 
3.3%
Other values (31) 4290230
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9854462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 916177
 
9.3%
/ 841631
 
8.5%
a 801117
 
8.1%
e 553122
 
5.6%
o 447814
 
4.5%
u 442847
 
4.5%
w 442001
 
4.5%
i 433008
 
4.4%
. 360699
 
3.7%
l 325816
 
3.3%
Other values (31) 4290230
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9854462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 916177
 
9.3%
/ 841631
 
8.5%
a 801117
 
8.1%
e 553122
 
5.6%
o 447814
 
4.5%
u 442847
 
4.5%
w 442001
 
4.5%
i 433008
 
4.4%
. 360699
 
3.7%
l 325816
 
3.3%
Other values (31) 4290230
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9854462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 916177
 
9.3%
/ 841631
 
8.5%
a 801117
 
8.1%
e 553122
 
5.6%
o 447814
 
4.5%
u 442847
 
4.5%
w 442001
 
4.5%
i 433008
 
4.4%
. 360699
 
3.7%
l 325816
 
3.3%
Other values (31) 4290230
43.5%

team
Categorical

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NE
 
7872
JAX
 
7626
PHI
 
7601
BAL
 
7535
GB
 
7478
Other values (27)
192007 

Length

Max length3
Median length3
Mean length2.7471656
Min length2

Characters and Unicode

Total characters632175
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC
2nd rowDET
3rd rowNYJ
4th rowCHI
5th rowCLE

Common Values

ValueCountFrequency (%)
NE 7872
 
3.4%
JAX 7626
 
3.3%
PHI 7601
 
3.3%
BAL 7535
 
3.3%
GB 7478
 
3.2%
IND 7468
 
3.2%
SEA 7390
 
3.2%
DAL 7333
 
3.2%
CIN 7254
 
3.2%
NYG 7231
 
3.1%
Other values (22) 155331
67.5%

Length

2024-08-08T20:45:20.797494image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 7872
 
3.4%
jax 7626
 
3.3%
phi 7601
 
3.3%
bal 7535
 
3.3%
gb 7478
 
3.2%
ind 7468
 
3.2%
sea 7390
 
3.2%
dal 7333
 
3.2%
cin 7254
 
3.2%
nyg 7231
 
3.1%
Other values (22) 155331
67.5%

Most occurring characters

ValueCountFrequency (%)
A 79522
12.6%
N 65324
 
10.3%
I 57282
 
9.1%
L 50496
 
8.0%
E 43838
 
6.9%
C 42818
 
6.8%
T 35792
 
5.7%
B 29207
 
4.6%
D 29113
 
4.6%
S 21454
 
3.4%
Other values (14) 177329
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 632175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 79522
12.6%
N 65324
 
10.3%
I 57282
 
9.1%
L 50496
 
8.0%
E 43838
 
6.9%
C 42818
 
6.8%
T 35792
 
5.7%
B 29207
 
4.6%
D 29113
 
4.6%
S 21454
 
3.4%
Other values (14) 177329
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 632175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 79522
12.6%
N 65324
 
10.3%
I 57282
 
9.1%
L 50496
 
8.0%
E 43838
 
6.9%
C 42818
 
6.8%
T 35792
 
5.7%
B 29207
 
4.6%
D 29113
 
4.6%
S 21454
 
3.4%
Other values (14) 177329
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 632175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 79522
12.6%
N 65324
 
10.3%
I 57282
 
9.1%
L 50496
 
8.0%
E 43838
 
6.9%
C 42818
 
6.8%
T 35792
 
5.7%
B 29207
 
4.6%
D 29113
 
4.6%
S 21454
 
3.4%
Other values (14) 177329
28.1%

def_tackles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4921671
Minimum0
Maximum20
Zeros29880
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:21.082756image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1270859
Coefficient of variation (CV)0.85350855
Kurtosis1.7667763
Mean2.4921671
Median Absolute Deviation (MAD)1
Skewness1.2463793
Sum573495
Variance4.5244945
MonotonicityNot monotonic
2024-08-08T20:45:21.190854image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 65816
28.6%
2 42612
18.5%
3 31643
13.8%
0 29880
13.0%
4 22507
 
9.8%
5 15140
 
6.6%
6 9663
 
4.2%
7 5896
 
2.6%
8 3377
 
1.5%
9 1805
 
0.8%
Other values (11) 1780
 
0.8%
ValueCountFrequency (%)
0 29880
13.0%
1 65816
28.6%
2 42612
18.5%
3 31643
13.8%
4 22507
 
9.8%
5 15140
 
6.6%
6 9663
 
4.2%
7 5896
 
2.6%
8 3377
 
1.5%
9 1805
 
0.8%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 5
 
< 0.1%
16 10
 
< 0.1%
15 13
 
< 0.1%
14 36
 
< 0.1%
13 102
 
< 0.1%
12 193
0.1%
11 464
0.2%

def_tackles_solo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1279816
Minimum0
Maximum16
Zeros39957
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:21.294949image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9059349
Coefficient of variation (CV)0.89565382
Kurtosis1.8852624
Mean2.1279816
Median Absolute Deviation (MAD)1
Skewness1.2804424
Sum489689
Variance3.6325877
MonotonicityNot monotonic
2024-08-08T20:45:21.398042image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 70345
30.6%
2 43210
18.8%
0 39957
17.4%
3 29787
12.9%
4 19809
 
8.6%
5 12193
 
5.3%
6 7111
 
3.1%
7 3948
 
1.7%
8 2029
 
0.9%
9 976
 
0.4%
Other values (7) 754
 
0.3%
ValueCountFrequency (%)
0 39957
17.4%
1 70345
30.6%
2 43210
18.8%
3 29787
12.9%
4 19809
 
8.6%
5 12193
 
5.3%
6 7111
 
3.1%
7 3948
 
1.7%
8 2029
 
0.9%
9 976
 
0.4%
ValueCountFrequency (%)
16 2
 
< 0.1%
15 3
 
< 0.1%
14 14
 
< 0.1%
13 29
 
< 0.1%
12 65
 
< 0.1%
11 204
 
0.1%
10 437
 
0.2%
9 976
 
0.4%
8 2029
0.9%
7 3948
1.7%

def_tackles_with_assist
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36418549
Minimum0
Maximum10
Zeros173784
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:21.503138image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77258077
Coefficient of variation (CV)2.1213936
Kurtosis11.590186
Mean0.36418549
Median Absolute Deviation (MAD)0
Skewness2.9121002
Sum83806
Variance0.59688104
MonotonicityNot monotonic
2024-08-08T20:45:21.598225image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 173784
75.5%
1 38306
 
16.6%
2 11918
 
5.2%
3 3962
 
1.7%
4 1375
 
0.6%
5 500
 
0.2%
6 191
 
0.1%
7 45
 
< 0.1%
8 27
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
0 173784
75.5%
1 38306
 
16.6%
2 11918
 
5.2%
3 3962
 
1.7%
4 1375
 
0.6%
5 500
 
0.2%
6 191
 
0.1%
7 45
 
< 0.1%
8 27
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 9
 
< 0.1%
8 27
 
< 0.1%
7 45
 
< 0.1%
6 191
 
0.1%
5 500
 
0.2%
4 1375
 
0.6%
3 3962
 
1.7%
2 11918
 
5.2%
1 38306
16.6%

def_tackle_assists
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91430521
Minimum0
Maximum15
Zeros112462
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:21.691308image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2279077
Coefficient of variation (CV)1.3429954
Kurtosis5.4380118
Mean0.91430521
Median Absolute Deviation (MAD)1
Skewness1.9417321
Sum210399
Variance1.5077573
MonotonicityNot monotonic
2024-08-08T20:45:21.788396image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 112462
48.9%
1 65498
28.5%
2 29267
 
12.7%
3 12894
 
5.6%
4 5614
 
2.4%
5 2502
 
1.1%
6 1052
 
0.5%
7 476
 
0.2%
8 217
 
0.1%
9 73
 
< 0.1%
Other values (5) 64
 
< 0.1%
ValueCountFrequency (%)
0 112462
48.9%
1 65498
28.5%
2 29267
 
12.7%
3 12894
 
5.6%
4 5614
 
2.4%
5 2502
 
1.1%
6 1052
 
0.5%
7 476
 
0.2%
8 217
 
0.1%
9 73
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
13 6
 
< 0.1%
12 5
 
< 0.1%
11 9
 
< 0.1%
10 43
 
< 0.1%
9 73
 
< 0.1%
8 217
 
0.1%
7 476
 
0.2%
6 1052
0.5%
5 2502
1.1%

def_tackles_for_loss
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23984547
Minimum0
Maximum6
Zeros185400
Zeros (%)80.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:21.880480image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5400515
Coefficient of variation (CV)2.2516644
Kurtosis8.0549261
Mean0.23984547
Median Absolute Deviation (MAD)0
Skewness2.5946536
Sum55193
Variance0.29165562
MonotonicityNot monotonic
2024-08-08T20:45:21.970562image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 185400
80.6%
1 36036
 
15.7%
2 7161
 
3.1%
3 1293
 
0.6%
4 195
 
0.1%
5 28
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
0 185400
80.6%
1 36036
 
15.7%
2 7161
 
3.1%
3 1293
 
0.6%
4 195
 
0.1%
5 28
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
6 6
 
< 0.1%
5 28
 
< 0.1%
4 195
 
0.1%
3 1293
 
0.6%
2 7161
 
3.1%
1 36036
 
15.7%
0 185400
80.6%

def_tackles_for_loss_yards
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26175587
Minimum0
Maximum28
Zeros206176
Zeros (%)89.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:22.067650image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98162782
Coefficient of variation (CV)3.7501654
Kurtosis46.796793
Mean0.26175587
Median Absolute Deviation (MAD)0
Skewness5.6227965
Sum60235
Variance0.96359318
MonotonicityNot monotonic
2024-08-08T20:45:22.162737image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 206176
89.6%
1 9073
 
3.9%
2 5901
 
2.6%
3 3706
 
1.6%
4 2293
 
1.0%
5 1284
 
0.6%
6 701
 
0.3%
7 397
 
0.2%
8 250
 
0.1%
9 119
 
0.1%
Other values (15) 219
 
0.1%
ValueCountFrequency (%)
0 206176
89.6%
1 9073
 
3.9%
2 5901
 
2.6%
3 3706
 
1.6%
4 2293
 
1.0%
5 1284
 
0.6%
6 701
 
0.3%
7 397
 
0.2%
8 250
 
0.1%
9 119
 
0.1%
ValueCountFrequency (%)
28 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
23 1
 
< 0.1%
20 2
< 0.1%
19 1
 
< 0.1%
18 2
< 0.1%
17 2
< 0.1%
16 4
< 0.1%
15 3
< 0.1%

def_fumbles_forced
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
219393 
1
 
10270
2
 
434
3
 
21
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 219393
95.3%
1 10270
 
4.5%
2 434
 
0.2%
3 21
 
< 0.1%
4 1
 
< 0.1%

Length

2024-08-08T20:45:22.264829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:22.348905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 219393
95.3%
1 10270
 
4.5%
2 434
 
0.2%
3 21
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 219393
95.3%
1 10270
 
4.5%
2 434
 
0.2%
3 21
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 219393
95.3%
1 10270
 
4.5%
2 434
 
0.2%
3 21
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 219393
95.3%
1 10270
 
4.5%
2 434
 
0.2%
3 21
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 219393
95.3%
1 10270
 
4.5%
2 434
 
0.2%
3 21
 
< 0.1%
4 1
 
< 0.1%

def_sacks
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13384814
Minimum0
Maximum6
Zeros201981
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:22.433983image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39706116
Coefficient of variation (CV)2.9665049
Kurtosis15.334061
Mean0.13384814
Median Absolute Deviation (MAD)0
Skewness3.5280663
Sum30801
Variance0.15765756
MonotonicityNot monotonic
2024-08-08T20:45:22.525065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 201981
87.8%
1 19626
 
8.5%
0.5 4009
 
1.7%
2 2744
 
1.2%
1.5 1055
 
0.5%
3 425
 
0.2%
2.5 193
 
0.1%
4 37
 
< 0.1%
3.5 34
 
< 0.1%
4.5 6
 
< 0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
0 201981
87.8%
0.5 4009
 
1.7%
1 19626
 
8.5%
1.5 1055
 
0.5%
2 2744
 
1.2%
2.5 193
 
0.1%
3 425
 
0.2%
3.5 34
 
< 0.1%
4 37
 
< 0.1%
4.5 6
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5.5 1
 
< 0.1%
5 5
 
< 0.1%
4.5 6
 
< 0.1%
4 37
 
< 0.1%
3.5 34
 
< 0.1%
3 425
 
0.2%
2.5 193
 
0.1%
2 2744
1.2%
1.5 1055
 
0.5%

def_sack_yards
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86982822
Minimum0
Maximum46
Zeros203330
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:22.638168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum46
Range46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8903707
Coefficient of variation (CV)3.3229213
Kurtosis22.917571
Mean0.86982822
Median Absolute Deviation (MAD)0
Skewness4.26712
Sum200164
Variance8.3542431
MonotonicityNot monotonic
2024-08-08T20:45:22.759280image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203330
88.4%
7 2673
 
1.2%
8 2656
 
1.2%
6 2323
 
1.0%
9 2169
 
0.9%
5 2068
 
0.9%
4 1870
 
0.8%
10 1592
 
0.7%
3 1582
 
0.7%
2 1270
 
0.6%
Other values (66) 8586
 
3.7%
ValueCountFrequency (%)
0 203330
88.4%
0.5 195
 
0.1%
1 1186
 
0.5%
1.5 227
 
0.1%
2 1270
 
0.6%
2.5 422
 
0.2%
3 1582
 
0.7%
3.5 552
 
0.2%
4 1870
 
0.8%
4.5 474
 
0.2%
ValueCountFrequency (%)
46 1
 
< 0.1%
45 2
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 2
< 0.1%
41 1
 
< 0.1%
38 2
< 0.1%
37 3
< 0.1%
36 2
< 0.1%
35.5 3
< 0.1%

def_qb_hits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24660284
Minimum0
Maximum10
Zeros188368
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:22.860373image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6051682
Coefficient of variation (CV)2.4540195
Kurtosis13.890456
Mean0.24660284
Median Absolute Deviation (MAD)0
Skewness3.2126114
Sum56748
Variance0.36622855
MonotonicityNot monotonic
2024-08-08T20:45:22.959465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 188368
81.9%
1 30880
 
13.4%
2 7921
 
3.4%
3 2101
 
0.9%
4 618
 
0.3%
5 162
 
0.1%
6 50
 
< 0.1%
7 14
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 188368
81.9%
1 30880
 
13.4%
2 7921
 
3.4%
3 2101
 
0.9%
4 618
 
0.3%
5 162
 
0.1%
6 50
 
< 0.1%
7 14
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
< 0.1%
7 14
 
< 0.1%
6 50
 
< 0.1%
5 162
 
0.1%
4 618
 
0.3%
3 2101
 
0.9%
2 7921
 
3.4%
1 30880
13.4%

def_interceptions
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
218460 
1
 
10864
2
 
753
3
 
40
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 218460
94.9%
1 10864
 
4.7%
2 753
 
0.3%
3 40
 
< 0.1%
4 2
 
< 0.1%

Length

2024-08-08T20:45:23.061562image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:23.144639image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 218460
94.9%
1 10864
 
4.7%
2 753
 
0.3%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 218460
94.9%
1 10864
 
4.7%
2 753
 
0.3%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 218460
94.9%
1 10864
 
4.7%
2 753
 
0.3%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 218460
94.9%
1 10864
 
4.7%
2 753
 
0.3%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 218460
94.9%
1 10864
 
4.7%
2 753
 
0.3%
3 40
 
< 0.1%
4 2
 
< 0.1%

def_interception_yards
Real number (ℝ)

ZEROS 

Distinct136
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75902468
Minimum-100
Maximum150
Zeros221933
Zeros (%)96.4%
Negative236
Negative (%)0.1%
Memory size1.8 MiB
2024-08-08T20:45:23.249739image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum150
Range250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5216052
Coefficient of variation (CV)7.2746057
Kurtosis126.7972
Mean0.75902468
Median Absolute Deviation (MAD)0
Skewness10.065663
Sum174666
Variance30.488124
MonotonicityNot monotonic
2024-08-08T20:45:23.373852image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 221933
96.4%
1 408
 
0.2%
2 390
 
0.2%
3 330
 
0.1%
4 305
 
0.1%
5 278
 
0.1%
7 257
 
0.1%
6 249
 
0.1%
8 213
 
0.1%
9 213
 
0.1%
Other values (126) 5543
 
2.4%
ValueCountFrequency (%)
-100 1
 
< 0.1%
-23 1
 
< 0.1%
-18 1
 
< 0.1%
-16 1
 
< 0.1%
-15 1
 
< 0.1%
-12 3
< 0.1%
-11 1
 
< 0.1%
-10 1
 
< 0.1%
-9 2
< 0.1%
-8 4
< 0.1%
ValueCountFrequency (%)
150 1
 
< 0.1%
135 1
 
< 0.1%
127 1
 
< 0.1%
123 1
 
< 0.1%
122 1
 
< 0.1%
118 1
 
< 0.1%
116 1
 
< 0.1%
115 3
< 0.1%
114 1
 
< 0.1%
112 1
 
< 0.1%

def_pass_defended
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27326296
Minimum0
Maximum7
Zeros181209
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:23.469256image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.59437113
Coefficient of variation (CV)2.1750885
Kurtosis8.741111
Mean0.27326296
Median Absolute Deviation (MAD)0
Skewness2.6319193
Sum62883
Variance0.35327704
MonotonicityNot monotonic
2024-08-08T20:45:23.564344image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 181209
78.7%
1 37966
 
16.5%
2 8555
 
3.7%
3 1877
 
0.8%
4 410
 
0.2%
5 78
 
< 0.1%
6 22
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 181209
78.7%
1 37966
 
16.5%
2 8555
 
3.7%
3 1877
 
0.8%
4 410
 
0.2%
5 78
 
< 0.1%
6 22
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 22
 
< 0.1%
5 78
 
< 0.1%
4 410
 
0.2%
3 1877
 
0.8%
2 8555
 
3.7%
1 37966
 
16.5%
0 181209
78.7%

def_tds
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
228189 
1
 
1907
2
 
22
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 228189
99.2%
1 1907
 
0.8%
2 22
 
< 0.1%
3 1
 
< 0.1%

Length

2024-08-08T20:45:23.668440image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:23.747512image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 228189
99.2%
1 1907
 
0.8%
2 22
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 228189
99.2%
1 1907
 
0.8%
2 22
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 228189
99.2%
1 1907
 
0.8%
2 22
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 228189
99.2%
1 1907
 
0.8%
2 22
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 228189
99.2%
1 1907
 
0.8%
2 22
 
< 0.1%
3 1
 
< 0.1%

def_fumbles
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
229771 
1
 
337
2
 
7
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 229771
99.8%
1 337
 
0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%

Length

2024-08-08T20:45:23.835592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:23.915665image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 229771
99.8%
1 337
 
0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 229771
99.8%
1 337
 
0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 229771
99.8%
1 337
 
0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 229771
99.8%
1 337
 
0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 229771
99.8%
1 337
 
0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%

def_fumble_recovery_own
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
229725 
1
 
393
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 229725
99.8%
1 393
 
0.2%
2 1
 
< 0.1%

Length

2024-08-08T20:45:24.004747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:24.081817image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 229725
99.8%
1 393
 
0.2%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 229725
99.8%
1 393
 
0.2%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 229725
99.8%
1 393
 
0.2%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 229725
99.8%
1 393
 
0.2%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 229725
99.8%
1 393
 
0.2%
2 1
 
< 0.1%

def_fumble_recovery_yards_own
Real number (ℝ)

SKEWED  ZEROS 

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0056101408
Minimum-16
Maximum64
Zeros230000
Zeros (%)99.9%
Negative18
Negative (%)< 0.1%
Memory size1.8 MiB
2024-08-08T20:45:24.175912image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum64
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43272474
Coefficient of variation (CV)77.132599
Kurtosis11702.349
Mean0.0056101408
Median Absolute Deviation (MAD)0
Skewness98.879296
Sum1291
Variance0.1872507
MonotonicityNot monotonic
2024-08-08T20:45:24.298035image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 230000
99.9%
1 10
 
< 0.1%
2 8
 
< 0.1%
7 7
 
< 0.1%
9 6
 
< 0.1%
11 6
 
< 0.1%
3 6
 
< 0.1%
5 6
 
< 0.1%
8 6
 
< 0.1%
4 6
 
< 0.1%
Other values (31) 58
 
< 0.1%
ValueCountFrequency (%)
-16 2
 
< 0.1%
-8 2
 
< 0.1%
-7 1
 
< 0.1%
-5 4
 
< 0.1%
-4 1
 
< 0.1%
-3 1
 
< 0.1%
-2 5
 
< 0.1%
-1 2
 
< 0.1%
0 230000
99.9%
1 10
 
< 0.1%
ValueCountFrequency (%)
64 1
< 0.1%
63 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
53 1
< 0.1%
51 1
< 0.1%
48 1
< 0.1%
47 2
< 0.1%
44 1
< 0.1%
31 1
< 0.1%

def_fumble_recovery_opp
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
222941 
1
 
7049
2
 
127
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 222941
96.9%
1 7049
 
3.1%
2 127
 
0.1%
3 2
 
< 0.1%

Length

2024-08-08T20:45:24.412152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:24.492229image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 222941
96.9%
1 7049
 
3.1%
2 127
 
0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 222941
96.9%
1 7049
 
3.1%
2 127
 
0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 222941
96.9%
1 7049
 
3.1%
2 127
 
0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 222941
96.9%
1 7049
 
3.1%
2 127
 
0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 222941
96.9%
1 7049
 
3.1%
2 127
 
0.1%
3 2
 
< 0.1%

def_fumble_recovery_yards_opp
Real number (ℝ)

SKEWED  ZEROS 

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15733164
Minimum-26
Maximum104
Zeros227993
Zeros (%)99.1%
Negative108
Negative (%)< 0.1%
Memory size1.8 MiB
2024-08-08T20:45:24.597326image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-26
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum104
Range130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5057828
Coefficient of variation (CV)15.926757
Kurtosis586.83945
Mean0.15733164
Median Absolute Deviation (MAD)0
Skewness22.153694
Sum36205
Variance6.2789474
MonotonicityNot monotonic
2024-08-08T20:45:24.722452image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227993
99.1%
1 180
 
0.1%
2 151
 
0.1%
3 130
 
0.1%
4 116
 
0.1%
5 101
 
< 0.1%
6 87
 
< 0.1%
8 70
 
< 0.1%
7 62
 
< 0.1%
10 61
 
< 0.1%
Other values (101) 1168
 
0.5%
ValueCountFrequency (%)
-26 1
 
< 0.1%
-16 1
 
< 0.1%
-9 1
 
< 0.1%
-8 2
 
< 0.1%
-7 2
 
< 0.1%
-6 4
 
< 0.1%
-5 9
 
< 0.1%
-4 6
 
< 0.1%
-3 18
< 0.1%
-2 29
< 0.1%
ValueCountFrequency (%)
104 1
 
< 0.1%
102 1
 
< 0.1%
100 1
 
< 0.1%
99 1
 
< 0.1%
98 3
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 2
< 0.1%
94 2
< 0.1%
93 2
< 0.1%

def_safety
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
229879 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters230119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 229879
99.9%
1 240
 
0.1%

Length

2024-08-08T20:45:24.830074image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:45:24.904144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 229879
99.9%
1 240
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 229879
99.9%
1 240
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 229879
99.9%
1 240
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 229879
99.9%
1 240
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 229879
99.9%
1 240
 
0.1%

def_penalty
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15807908
Minimum0
Maximum5
Zeros197162
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:24.976218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40724412
Coefficient of variation (CV)2.576205
Kurtosis8.2089303
Mean0.15807908
Median Absolute Deviation (MAD)0
Skewness2.7188583
Sum36377
Variance0.16584777
MonotonicityNot monotonic
2024-08-08T20:45:25.071484image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 197162
85.7%
1 29857
 
13.0%
2 2808
 
1.2%
3 265
 
0.1%
4 26
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 197162
85.7%
1 29857
 
13.0%
2 2808
 
1.2%
3 265
 
0.1%
4 26
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 26
 
< 0.1%
3 265
 
0.1%
2 2808
 
1.2%
1 29857
 
13.0%
0 197162
85.7%

def_penalty_yards
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.482685
Minimum0
Maximum106
Zeros197476
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-08-08T20:45:25.195597image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum106
Range106
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7382915
Coefficient of variation (CV)3.1957505
Kurtosis34.623581
Mean1.482685
Median Absolute Deviation (MAD)0
Skewness4.9086088
Sum341194
Variance22.451406
MonotonicityNot monotonic
2024-08-08T20:45:25.346896image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 197476
85.8%
5 13155
 
5.7%
15 6215
 
2.7%
10 3815
 
1.7%
4 737
 
0.3%
6 652
 
0.3%
20 651
 
0.3%
9 528
 
0.2%
8 526
 
0.2%
7 493
 
0.2%
Other values (70) 5871
 
2.6%
ValueCountFrequency (%)
0 197476
85.8%
1 343
 
0.1%
2 398
 
0.2%
3 480
 
0.2%
4 737
 
0.3%
5 13155
 
5.7%
6 652
 
0.3%
7 493
 
0.2%
8 526
 
0.2%
9 528
 
0.2%
ValueCountFrequency (%)
106 1
 
< 0.1%
99 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
87 3
< 0.1%
80 1
 
< 0.1%
77 1
 
< 0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 3
< 0.1%

Interactions

2024-08-08T20:45:13.711276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:43.408967image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:45.303723image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:47.233796image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:49.303073image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:51.356712image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:53.439078image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.310521image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.157197image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:59.026620image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:01.005717image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:02.700738image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:04.422858image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:06.192520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:08.189305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:10.009991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:11.807885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:13.921467image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:43.550499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:45.398826image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:47.342909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:49.410694image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:51.468519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:53.545685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.411629image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.257708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:59.139275image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:01.100807image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:02.794768image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:04.520438image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:06.304626image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:08.291407image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:10.109535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:11.906975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:14.019556image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:43.666135image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:45.493935image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:47.482053image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:49.520793image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:51.577629image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:53.647779image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.508742image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.353323image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:59.240377image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:01.190420image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:02.890856image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:04.617530image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:06.412729image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:08.390503image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:10.209562image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:12.004062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:14.132658image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:43.782267image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:45.604054image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:47.637318image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:49.645917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:51.701755image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:53.766910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.624950image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.464438image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:59.360495image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:01.298530image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:03.001135image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:04.726633image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:06.530838image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:08.505118image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:10.324666image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:12.115165image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:14.242767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:43.901917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:45.722692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:47.771943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:49.768540image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:51.826814image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:53.884053image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.739594image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.578592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:59.478621image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:01.407183image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:03.115479image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:04.837738image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:06.841130image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:08.617733image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:10.440771image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:12.226264image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:14.345860image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:44.008023image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:45.823806image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:47.891603image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:49.888668image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:51.938430image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:53.992155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.850708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.680691image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:59.586731image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:01.502273image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:03.214569image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:04.939837image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:06.945229image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:08.722835image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:10.551872image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:12.324353image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:45:14.453958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:44.133144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:46.093100image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:48.011727image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:50.006773image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:52.052592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:54.105269image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:55.965821image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:44:57.790836image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-08T20:45:13.611185image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-08T20:45:25.457998image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
def_fumble_recovery_oppdef_fumble_recovery_owndef_fumble_recovery_yards_oppdef_fumble_recovery_yards_owndef_fumblesdef_fumbles_forceddef_interception_yardsdef_interceptionsdef_pass_defendeddef_penaltydef_penalty_yardsdef_qb_hitsdef_sack_yardsdef_sacksdef_safetydef_tackle_assistsdef_tacklesdef_tackles_for_lossdef_tackles_for_loss_yardsdef_tackles_solodef_tackles_with_assistdef_tdspositionposition_groupseasonseason_typeteamweek
def_fumble_recovery_opp1.0000.0330.3050.0380.0300.0590.0000.0020.0080.0030.0000.0090.0240.0310.0000.0110.0240.0120.0000.0240.0100.0940.0310.0270.0150.0030.0050.003
def_fumble_recovery_own0.0331.0000.0690.3650.2250.0000.0160.0160.0070.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0040.0000.0200.0400.0410.0100.0000.0130.002
def_fumble_recovery_yards_opp0.3050.0691.0000.1660.0440.0240.0060.0000.014-0.001-0.0010.0090.0140.0140.0000.0150.0250.0060.0020.0230.0090.2100.0420.023-0.0070.0050.004-0.006
def_fumble_recovery_yards_own0.0380.3650.1661.0000.1830.0000.0030.0000.000-0.003-0.0030.0020.0020.0020.0000.0030.0040.0020.0020.005-0.0020.0250.0000.005-0.0020.0050.005-0.003
def_fumbles0.0300.2250.0440.1831.0000.0000.0560.0550.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0030.0480.0480.0080.0000.0050.000
def_fumbles_forced0.0590.0000.0240.0000.0001.0000.0000.0000.0080.0030.0000.0480.1460.1800.0090.0130.0670.0300.0080.0740.0100.0090.0410.0280.0110.0000.0070.001
def_interception_yards0.0000.0160.0060.0030.0560.0001.0000.4290.3610.0070.008-0.042-0.030-0.0310.0000.0140.080-0.023-0.0140.0820.0160.3290.0630.062-0.0250.0030.0050.002
def_interceptions0.0020.0160.0000.0000.0550.0000.4291.0000.2850.0100.0150.0160.0160.0180.0000.0070.0500.0150.0020.0530.0060.1640.1060.1050.0140.0000.0060.002
def_pass_defended0.0080.0070.0140.0000.0250.0080.3610.2851.0000.0380.040-0.061-0.039-0.0400.0000.0270.181-0.022-0.0150.1830.0360.0810.1310.124-0.0330.0020.0110.001
def_penalty0.0030.000-0.001-0.0030.0000.0030.0070.0100.0381.0000.9910.0040.0030.0020.000-0.046-0.020-0.009-0.008-0.014-0.0260.0040.0630.048-0.0000.0050.012-0.018
def_penalty_yards0.0000.000-0.001-0.0030.0000.0000.0080.0150.0400.9911.0000.0020.001-0.0000.000-0.046-0.019-0.011-0.009-0.013-0.0260.0030.0460.0440.0000.0030.006-0.018
def_qb_hits0.0090.0000.0090.0020.0000.048-0.0420.016-0.0610.0040.0021.0000.6820.6960.0310.0690.0400.4010.0720.047-0.0070.0120.0770.0660.1280.0000.009-0.005
def_sack_yards0.0240.0000.0140.0020.0000.146-0.0300.016-0.0390.0030.0010.6821.0000.9720.0410.0600.1210.4950.0570.1310.0120.0060.0820.0730.0030.0000.008-0.002
def_sacks0.0310.0000.0140.0020.0000.180-0.0310.018-0.0400.002-0.0000.6960.9721.0000.0560.0620.1260.4880.0580.1360.0130.0000.0930.0840.0030.0000.008-0.002
def_safety0.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0310.0410.0561.0000.0250.0050.0540.1160.0090.0000.0000.0170.0170.0070.0000.0030.000
def_tackle_assists0.0110.0000.0150.0030.0000.0130.0140.0070.027-0.046-0.0460.0690.0600.0620.0251.0000.2010.0690.0670.1550.1570.0000.1070.0880.1250.0140.0230.013
def_tackles0.0240.0000.0250.0040.0000.0670.0800.0500.181-0.020-0.0190.0400.1210.1260.0050.2011.0000.2660.2180.9300.4020.0160.1410.138-0.0680.0000.012-0.003
def_tackles_for_loss0.0120.0010.0060.0020.0000.030-0.0230.015-0.022-0.009-0.0110.4010.4950.4880.0540.0690.2661.0000.7020.2560.0950.0000.1070.1010.0300.0000.012-0.002
def_tackles_for_loss_yards0.0000.0000.0020.0020.0000.008-0.0140.002-0.015-0.008-0.0090.0720.0570.0580.1160.0670.2180.7021.0000.1920.1220.0000.0340.0350.0030.0000.007-0.001
def_tackles_solo0.0240.0040.0230.0050.0030.0740.0820.0530.183-0.014-0.0130.0470.1310.1360.0090.1550.9300.2560.1921.0000.0900.0180.1500.148-0.0200.0000.019-0.007
def_tackles_with_assist0.0100.0000.009-0.0020.0000.0100.0160.0060.036-0.026-0.026-0.0070.0120.0130.0000.1570.4020.0950.1220.0901.0000.0000.0570.053-0.1390.0070.0340.009
def_tds0.0940.0200.2100.0250.0030.0090.3290.1640.0810.0040.0030.0120.0060.0000.0000.0000.0160.0000.0000.0180.0001.0000.0320.0310.0090.0040.0090.002
position0.0310.0400.0420.0000.0480.0410.0630.1060.1310.0630.0460.0770.0820.0930.0170.1070.1410.1070.0340.1500.0570.0321.0001.0000.1280.0130.0480.009
position_group0.0270.0410.0230.0050.0480.0280.0620.1050.1240.0480.0440.0660.0730.0840.0170.0880.1380.1010.0350.1480.0530.0311.0001.0000.0190.0050.0230.000
season0.0150.010-0.007-0.0020.0080.011-0.0250.014-0.033-0.0000.0000.1280.0030.0030.0070.125-0.0680.0300.003-0.020-0.1390.0090.1280.0191.0000.0060.0250.010
season_type0.0030.0000.0050.0050.0000.0000.0030.0000.0020.0050.0030.0000.0000.0000.0000.0140.0000.0000.0000.0000.0070.0040.0130.0050.0061.0000.0970.923
team0.0050.0130.0040.0050.0050.0070.0050.0060.0110.0120.0060.0090.0080.0080.0030.0230.0120.0120.0070.0190.0340.0090.0480.0230.0250.0971.0000.038
week0.0030.002-0.006-0.0030.0000.0010.0020.0020.001-0.018-0.018-0.005-0.002-0.0020.0000.013-0.003-0.002-0.001-0.0070.0090.0020.0090.0000.0100.9230.0381.000

Missing values

2024-08-08T20:45:15.766266image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-08T20:45:16.548335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-08T20:45:17.259829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seasonweekseason_typeplayer_idplayer_nameplayer_display_namepositionposition_groupheadshot_urlteamdef_tacklesdef_tackles_solodef_tackles_with_assistdef_tackle_assistsdef_tackles_for_lossdef_tackles_for_loss_yardsdef_fumbles_forceddef_sacksdef_sack_yardsdef_qb_hitsdef_interceptionsdef_interception_yardsdef_pass_defendeddef_tdsdef_fumblesdef_fumble_recovery_owndef_fumble_recovery_yards_owndef_fumble_recovery_oppdef_fumble_recovery_yards_oppdef_safetydef_penaltydef_penalty_yards
019991REG0NaNNaNNaNNaNNaNKC11000011.015.00000000000000
119991REG0NaNNaNNaNNaNNaNDET00000000.00.00000000000100
219991REG0NaNNaNNaNNaNNaNNYJ00000000.00.00000000000100
319991REG0NaNNaNNaNNaNNaNCHI00000000.00.00000000000015
419991REG0NaNNaNNaNNaNNaNCLE00000000.00.00000000000015
519991REG0NaNNaNNaNNaNNaNMIA00000000.00.00000000000015
619991REG0NaNNaNNaNNaNNaNPIT00000000.00.00000000000015
719991REG0NaNNaNNaNNaNNaNWAS00000000.00.00000000000015
819992REG0NaNNaNNaNNaNNaNCAR11000011.09.00000000000000
919992REG0NaNNaNNaNNaNNaNDET11000000.00.00000000000000
seasonweekseason_typeplayer_idplayer_nameplayer_display_namepositionposition_groupheadshot_urlteamdef_tacklesdef_tackles_solodef_tackles_with_assistdef_tackle_assistsdef_tackles_for_lossdef_tackles_for_loss_yardsdef_fumbles_forceddef_sacksdef_sack_yardsdef_qb_hitsdef_interceptionsdef_interception_yardsdef_pass_defendeddef_tdsdef_fumblesdef_fumble_recovery_owndef_fumble_recovery_yards_owndef_fumble_recovery_oppdef_fumble_recovery_yards_oppdef_safetydef_penaltydef_penalty_yards
23010920237REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA44000000.00.00001000000000
23011020238REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA11010000.00.00000000000015
23011120239REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA43120000.00.00001000000000
230112202310REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA44010010.00.00003000000000
230113202311REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA54131001.05.010010000000219
230114202312REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA42211500.00.00001000000000
230115202313REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA43140000.00.00001000000000
230116202314REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA00000000.00.00001000000000
230117202317REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA33001500.00.00000000000000
230118202318REG00-0039169D.WitherspoonDevon WitherspoonCBDBhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/vm13kgvkrqzjyf08s2n0SEA76143200.00.00000000000000